{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import 模块\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据集\n",
    "train_data = pd.read_csv(\"pima_indians_diabetes.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train : (768, 9)\n"
     ]
    }
   ],
   "source": [
    "# 测试数据集\n",
    "print(\"Train :\", train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   pregnants                     768 non-null    int64  \n",
      " 1   Plasma_glucose_concentration  768 non-null    int64  \n",
      " 2   blood_pressure                768 non-null    int64  \n",
      " 3   Triceps_skin_fold_thickness   768 non-null    int64  \n",
      " 4   serum_insulin                 768 non-null    int64  \n",
      " 5   BMI                           768 non-null    float64\n",
      " 6   Diabetes_pedigree_function    768 non-null    float64\n",
      " 7   Age                           768 non-null    int64  \n",
      " 8   Target                        768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据集基本信息\n",
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看特征的基本统计量\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train_data[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(10,9))\n",
    "data_corr = train_data.corr().abs()\n",
    "sns.heatmap(data_corr,annot=True,)\n",
    "plt.show()\n",
    "# 通过热力图并没有发现太多关联"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants_log</th>\n",
       "      <th>Plasma_glucose_concentration_log</th>\n",
       "      <th>blood_pressure_log</th>\n",
       "      <th>Triceps_skin_fold_thickness_log</th>\n",
       "      <th>serum_insulin_log</th>\n",
       "      <th>BMI_log</th>\n",
       "      <th>Diabetes_pedigree_function_log</th>\n",
       "      <th>Age_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.945910</td>\n",
       "      <td>5.003946</td>\n",
       "      <td>4.290459</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.543854</td>\n",
       "      <td>0.486738</td>\n",
       "      <td>3.931826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.693147</td>\n",
       "      <td>4.454347</td>\n",
       "      <td>4.204693</td>\n",
       "      <td>3.401197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.317816</td>\n",
       "      <td>0.300845</td>\n",
       "      <td>3.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>5.214936</td>\n",
       "      <td>4.174387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.190476</td>\n",
       "      <td>0.514021</td>\n",
       "      <td>3.496508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.693147</td>\n",
       "      <td>4.499810</td>\n",
       "      <td>4.204693</td>\n",
       "      <td>3.178054</td>\n",
       "      <td>4.553877</td>\n",
       "      <td>3.370738</td>\n",
       "      <td>0.154436</td>\n",
       "      <td>3.091042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.927254</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>5.129899</td>\n",
       "      <td>3.786460</td>\n",
       "      <td>1.190279</td>\n",
       "      <td>3.526361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants_log  Plasma_glucose_concentration_log  blood_pressure_log  \\\n",
       "0       1.945910                          5.003946            4.290459   \n",
       "1       0.693147                          4.454347            4.204693   \n",
       "2       2.197225                          5.214936            4.174387   \n",
       "3       0.693147                          4.499810            4.204693   \n",
       "4       0.000000                          4.927254            3.713572   \n",
       "\n",
       "   Triceps_skin_fold_thickness_log  serum_insulin_log   BMI_log  \\\n",
       "0                         3.583519           0.000000  3.543854   \n",
       "1                         3.401197           0.000000  3.317816   \n",
       "2                         0.000000           0.000000  3.190476   \n",
       "3                         3.178054           4.553877  3.370738   \n",
       "4                         3.583519           5.129899  3.786460   \n",
       "\n",
       "   Diabetes_pedigree_function_log   Age_log  \n",
       "0                        0.486738  3.931826  \n",
       "1                        0.300845  3.465736  \n",
       "2                        0.514021  3.496508  \n",
       "3                        0.154436  3.091042  \n",
       "4                        1.190279  3.526361  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=train_data['Target']\n",
    "_X=train_data.drop('Target',axis=1)\n",
    "_X=np.log1p(_X)\n",
    "cate=_X.columns+'_log'\n",
    "X=pd.DataFrame(columns=cate,data=_X.values)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants_log</th>\n",
       "      <th>Plasma_glucose_concentration_log</th>\n",
       "      <th>blood_pressure_log</th>\n",
       "      <th>Triceps_skin_fold_thickness_log</th>\n",
       "      <th>serum_insulin_log</th>\n",
       "      <th>BMI_log</th>\n",
       "      <th>Diabetes_pedigree_function_log</th>\n",
       "      <th>Age_log</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.673239</td>\n",
       "      <td>0.944441</td>\n",
       "      <td>0.891583</td>\n",
       "      <td>0.778151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.839581</td>\n",
       "      <td>0.356534</td>\n",
       "      <td>0.639050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.239812</td>\n",
       "      <td>0.840710</td>\n",
       "      <td>0.873760</td>\n",
       "      <td>0.738561</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.786030</td>\n",
       "      <td>0.195523</td>\n",
       "      <td>0.284791</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.760188</td>\n",
       "      <td>0.984263</td>\n",
       "      <td>0.867462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.755862</td>\n",
       "      <td>0.380165</td>\n",
       "      <td>0.308180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.239812</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.873760</td>\n",
       "      <td>0.690106</td>\n",
       "      <td>0.675479</td>\n",
       "      <td>0.798568</td>\n",
       "      <td>0.068711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.929966</td>\n",
       "      <td>0.771702</td>\n",
       "      <td>0.778151</td>\n",
       "      <td>0.760921</td>\n",
       "      <td>0.897058</td>\n",
       "      <td>0.965907</td>\n",
       "      <td>0.330870</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants_log  Plasma_glucose_concentration_log  blood_pressure_log  \\\n",
       "0       0.673239                          0.944441            0.891583   \n",
       "1       0.239812                          0.840710            0.873760   \n",
       "2       0.760188                          0.984263            0.867462   \n",
       "3       0.239812                          0.849290            0.873760   \n",
       "4       0.000000                          0.929966            0.771702   \n",
       "\n",
       "   Triceps_skin_fold_thickness_log  serum_insulin_log   BMI_log  \\\n",
       "0                         0.778151           0.000000  0.839581   \n",
       "1                         0.738561           0.000000  0.786030   \n",
       "2                         0.000000           0.000000  0.755862   \n",
       "3                         0.690106           0.675479  0.798568   \n",
       "4                         0.778151           0.760921  0.897058   \n",
       "\n",
       "   Diabetes_pedigree_function_log   Age_log  Target  \n",
       "0                        0.356534  0.639050       1  \n",
       "1                        0.195523  0.284791       0  \n",
       "2                        0.380165  0.308180       1  \n",
       "3                        0.068711  0.000000       0  \n",
       "4                        0.965907  0.330870       1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因为值的0较多所以用minmaxcaler做数据缩放\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "ms_log=MinMaxScaler()\n",
    "feat_names_log=X.columns\n",
    "X_train_log = ms_log.fit_transform(X)\n",
    "\n",
    "y = pd.Series(data = y, name = 'Target')\n",
    "train_log = pd.concat([pd.DataFrame(columns = feat_names_log, data = X_train_log), y], axis = 1)\n",
    "train_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [-0.72727273 -0.69480519 -0.68831169 -0.71895425 -0.69281046]\n",
      "cv logloss is:  -0.7044308632543927\n"
     ]
    }
   ],
   "source": [
    "# 用Logistic回归和五折交叉验证训练数据\n",
    "y_train=train_log['Target']\n",
    "X_train=train_log.drop(['Target'],axis=1)\n",
    "feat_names = X_train.columns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()\n",
    "from sklearn.model_selection import cross_val_score\n",
    "loss=cross_val_score(lr,X_train,y_train,cv=5) \n",
    "print('logloss of each fold is: ', -loss)\n",
    "print('cv logloss is: ', -loss.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5515214031714964\n",
      "{'C': 100, 'penalty': 'l2'}\n",
      "最佳得分:  -0.764230540701129\n",
      "{'C': 10, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=777)\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression(solver='liblinear')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=fold, scoring='neg_log_loss',n_jobs = 4,)\n",
    "grid.fit(X_train,y_train)\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "\n",
    "lr_penalty= LogisticRegression(solver='liblinear')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=fold, scoring='accuracy',n_jobs = 4,) #正确率\n",
    "grid.fit(X_train,y_train)\n",
    "print('最佳得分: ', -grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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